E-scooter services have multiplied worldwide as a form of urban transport. Their use has grown so quickly that policymakers and researchers still need to understand their interrelation with other transport modes. At present, e-scooter services are primarily seen as a first-and-last-mile solution for public transport. However, we demonstrate that \(50\,\%\) of e-scooter trips are either substituting it or covering areas with little public transportation infrastructure. To this end, we have developed a novel data-driven methodology that autonomously classifies e-scooter trips according to their relation to public transit. Instead of predefined design criteria, the blind nature of our approach extracts the city’s intrinsic parameters from real data. We applied this methodology to Rome (Italy), and our findings reveal that e-scooters provide specific mobility solutions in areas with particular needs. Thus, we believe that the proposed methodology will contribute to the understanding of e-scooter services as part of shared urban mobility.

作为一种城市交通形式，电动滑板车服务在全球范围内呈倍数增长。它们的使用增长如此之快，以至于政策制定者和研究人员仍然需要了解它们与其他运输方式的相互关系。目前，电动滑板车服务主要被视为公共交通的第一英里和最后一英里解决方案。然而，我们证明， \(50\,\%\)的电动滑板车出行要么取代了它，要么覆盖了公共交通基础设施很少的地区。为此，我们开发了一种新颖的数据驱动方法，可以根据电动滑板车出行与公共交通的关系自动对出行进行分类。我们方法的盲目性不是预先定义的设计标准，而是从真实数据中提取城市的内在参数。我们将这种方法应用于罗马（意大利），我们的研究结果表明，电动滑板车在有特殊需求的地区提供了特定的移动解决方案。因此，我们相信所提出的方法将有助于理解电动滑板车服务作为共享城市出行的一部分。